Alvaro A. A. Fernandes

Distributed Query Processing on the Grid

Distributed query processing (DQP) has been widely used in data intensive applications where data of relevance to users is stored in multiple locations. This paper argues: (i) that DQP can be important in the Grid, as a means of providing high-level, declarative languages for integrating data access and analysis; and (ii) that the Grid provides resource management facilities that are useful to developers of DQP systems. As well as discussing and illustrating how DQP technologies can be deployed within the Grid, the paper describes a prototype implementation of a DQP system running over Globus.

Adaptive Query Processing: A Survey

In wide-area database systems, which may be running on unpredictable and volatile environments (such as computational grids), it is difficult to produce efficient database query plans based on information available solely at compile time. A solution to this problem is to exploit information that becomes available at query runtime and adapt the query plan to changing conditions during execution. This paper presents a survey on adaptive query processing techniques, examining the opportunities they offer to modify a plan dynamically and classifying them into categories according to the problem they focus on, their objectives, the nature of feedback they collect from the environment, the frequency at which they can adapt, their implementation environment and which component is responsible for taking the adaptation decisions.

The design and implementation of OGSA-DQP: A service-based distributed query processor

Service-based approaches are rising to prominence because of their potential to meet the requirements for distributed application development in e-business and e-science. The emergence of a service-oriented view of hardware and software resources raises the question as to how database management systems and technologies can best be deployed or adapted for use in such an environment. This paper explores one aspect of service-based computing and data management, viz., how to integrate query processing technology with a service-based architecture suitable for a Grid environment. The paper addresses this by describing in detail the design and implementation of a service-based distributed query processor. The query processor is service-based in two orthogonal senses: firstly, it supports querying over data storage and analysis resources that are made available as services, and, secondly, its internal architecture factors out as services the functionalities related to the construction and execution of distributed query plans. The resulting system both provides a declarative approach to service orchestration, and demonstrates how query processing can benefit from a service-based architecture. As well as describing and motivating the architecture used, the paper also describes usage scenarios, and, using a bioinformatics application, presents performance results that benchmark the system and illustrate the benefits provided by the service-based architecture.

Service-Based Distributed Querying on the Grid

Service-based approaches (such as Web Services and the Open Grid Services Architecture) have gained considerable attention recently for supporting distributed application development in e-business and e-science. The emergence of a service-oriented view of hardware and software resources raises the question as to how database management systems and technologies can best be deployed or adapted for use in such an environment. This paper explores one aspect of service-based computing and data management, viz., how to integrate query processing technology with a service-based Grid. The paper describes in detail the design and implementation of a service-based distributed query processor for the Grid. The query processor is service-based in two orthogonal senses: firstly, it supports querying over data storage and analysis resources that are made available as services, and, secondly, its internal architecture factors out as services the functionalities related to the construction of distributed query plans on the one hand, and to their execution over the Grid on the other. The resulting system both provides a declarative approach to service orchestration in the Grid, and demonstrates how query processing can benefit from dynamic access to computational resources on the Grid.

OGSA-DQP: A Service for Distributed Querying on the Grid

OGSA-DQP is a distributed query processor exposed to users as an Open Grid Services Architecture (OGSA)-compliant Grid service. This service supports the compilation and evaluation of queries that combine data obtained from multiple services on the Grid, includ- ing Grid Database Services (GDSs) and computational web services. Not only does OGSA-DQP support integrated access to multiple Grid ser- vices, it is itself implemented as a collection of interacting Grid services. OGSA-DQP illustrates how Grid service orchestrations can be used to perform complex, data-intensive parallel computations. The OGSA-DQP prototype is downloadable from www.ogsadai.org.uk/dqp/. This demon- stration aims to illustrate the capabilities of OGSA-DQP prototype via a GUI Client over a collection of bioinformatics databases and analysis tools.

Chapter 23 – Enhancing Services and Applications with Knowledge and Semantics

Publisher Summary This chapter introduces the motivations, technologies, and research challenges that underlie knowledge both on and for the Grid. It focuses on the aspects involved in building a knowledge-rich Grid in which services and applications are able at all levels to benefit from a coordinated and distributed collection of knowledge services founded upon the explicit representation and the explicit use of different forms of knowledge For a computational entity to interact fully with any other entity, making informed intelligent and possibly autonomous decisions, it must be able to exploit knowledge about those entities. Rich declarative models of knowledge are relevant to making decisions in the Grid environment and must be uniformly available to the system at any point. Intelligent reasoners access these knowledge sources to make informed decisions about requirements, resources, and processing and to reformulate such decisions in light of changes in the highly dynamic Grid environment, where execution failures and new resources are commonplace. Knowledge-oriented Grids are increasingly recognized as an important stage in the evolution of Grid computing, with their promise of semantic interoperability, intelligent automation and guidance, and smart reuse. By exploiting knowledge-rich models of information it can be hoped that Grid middleware may become more flexible and more robust.

Design and implementation of ROCK & ROLL: a deductive object-oriented database system

This paper presents an approach to the development of a deductive object-oriented database system, describing the key design decisions and their consequences for implementation. The approach is novel, in that it integrates an object-oriented database system manipulated using an imperative programming language (ROCK) with a logic language for expressing queries and methods (ROLL). The integration is made seamless by deriving both the imperative and logic languages from a single formally defined data model, thereby avoiding impedance mismatches when they are integrated.

A semantically enabled service architecture for mashups over streaming and stored data

Sensing devices are increasingly being deployed to monitor the physical world around us. One class of application for which sensor data is pertinent is environmental decision support systems, e.g. flood emergency response. However, in order to interpret the readings from the sensors, the data needs to be put in context through correlation with other sensor readings, sensor data histories, and stored data, as well as juxtaposing with maps and forecast models. In this paper we use a flood emergency response planning application to identify requirements for a semantic sensor web. We propose a generic service architecture to satisfy the requirements that uses semantic annotations to support well-informed interactions between the services. We present the SemSor- Grid4Env realisation of the architecture and illustrate its capabilities in the context of the example application.

A logic-based integration of active and deductive databases

A logic-based approach to the specification of active database functionality is presented which not only endows active databases with a well-defined and well-understood formal semantics, but also tightly integrates them with deductive databases. The problem of endowing deductive databases with rule-based active behaviour has been addressed in different ways. Typical approaches include accounting for active behaviour by extending the operational semantics of deductive databases, or, conversely, accounting for deductive capabilities by constraining the operational semantics of active databases. The main contribution of the paper is an alternative approach in which a class of active databases is defined whose operational semantics is naturally integrated with the operational semantics of deductive databases without either of them strictly subsuming the other. The approach is demonstrated via the formalization of the syntax and semantics of an active-rule language that can be smoothly incorporated into existing deductive databases, due to the fact that the standard formalization of deductive databases is reused, rather than altered or extended. One distinctive feature of the paper is its use of ahistory, as defined in the Kowalski-Sergot event-calculus, to define event occurrences, database states and actions on these. This has proved to be a suitable foundation for a comprehensive logical account of the concept set underpinning active databases. The paper thus contributes a logical perspective to the ongoing task of developing a formal theory of active databases.

Workflow adaptation as an autonomic computing problem

The performance of long running scientific workflows stands to benefit from adapting to changes in their environment. Autonomic Computing provides methodologies for managing run-time adaptations in managed systems. In this paper, we apply the monitoring, analysis, planning and execution (MAPE) model from autonomic computing to support the runtime modification of workflows with the aim of improving their performance. We systematically identify run-time adaptations and indicate how such behaviours can be captured using the MAPE model from the Autonomic Computing community. By characterising these as autonomic computing problems we make a proposal about how workflow adaptation can be achieved.